This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. MotivationBehind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learni...Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

published:28 Jul 2017

views:691

In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but the derivation is the same, and easily extensible.
Helpful diagram: https://www.dropbox.com/s/vj0qg9jlmy3mwof/Explanation_1.pdf?dl=0
This particular video goes from the derivative of the sigmoid itself to the delta for the output layer
The presentation can be found here: https://www.dropbox.com/s/z5bz0cw0boxxon1/BackPropagation.pdf?dl=0

published:07 Jan 2012

views:221941

Get free deep learning resources: https://goo.gl/Z6vLDU
Explore the basics behind convolutional neural networks (CNNs) in this MATLAB® Tech Talk. Broadly, CNNs are a common deep learning architecture – but what exactly is a CNN? This video breaks down this sometimes complicated concept into easy-to-understand parts. You’ll learn about 3 concepts: local receptive fields, shared weights and biases, and activation and pooling.
The video pulls together these three concepts and shows you how to configure the layers in a CNN.
You’ll also learn about the 3 ways to train CNNs for image analysis. These include: 1.) Training the model from scratch; 2.) Using transfer learning (based on the idea that you can use knowledge of one type of problem to solve a similar problem); 3.) Using a pretrained CNN to extract features for training a machine learning model.

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

published:01 Jun 2017

views:4346

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is female, it is technically referred to as a lioness. Since this model does not take all necessary features into account while performing classification, it is said to underfit the data. The problem of underfitting is solved by adding more detail to the model to ensure that it properly captures the differences between classes.
On the other hand, a model may also consider every possible detail and develop very specific, complex rules for classification. For example, if one data point represents a lion that is 3.5 feet tall, weighs 305 pounds, and has 2.9-inch claws, the model may develop a rule that classifies every 3.5 foot tall, 305 pound cat with 2.9-inch claws as a lion. Such rules will accurately classify the training data, but will poorly generalize to new data samples. A model that develops these kinds of rules is said to overfit the data. In other words, the model has failed to identify the true patterns that differentiate the classes. As a separate example, if the data only contained tigers that grew up in a zoo, the model may have difficulty classifying tigers that grew up in the wild. So while improving the process of data collection is helpful to prevent this problem, the model must be designed to identify the most important patterns that identify a class, so that new samples can be properly classified.
With regards to neural networks, overfitting typically stems from too many input features, or the use of an overly-complicated network configuration. If the input count is too large, the training process may start to assign weights to features that either aren't needed or add unnecessary complexity to the model. An overly-complicated configuration may lead to the development of specific rules that improperly relate many different features, resulting in poor generalization.
Overfitting is a common problem in data science. One popular method to reduce overfitting is the use of a cross-validation data set along with parameter averaging. For neural networks, a common method is regularization. There are different types such as L1 and L2, but each of these follows the same principle – penalize the model for letting weights and biases become too large. Another method is MaxNorm constraints, which directly adds a size limit to the weights and biases. A different approach is dropout, which randomly switches off certain neurons in the network, preventing the model from becoming too dependent on a set of neurons and the associated weights and biases. While these methods are broadly applied across the model rather than used for systematically searching for problem patterns, they have been proven to reduce and sometimes prevent the problem of overfitting.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

published:03 Jun 2016

views:13263

Excluding the beat; this song was written 100% by a deep neural network (if you aren't familiar with neural networks, just think artificial intelligence).
Source Code: https://github.com/robbiebarrat/rapping-neural-network
Lyrics: I wrote a neural network, which wrote this after being introduced to ~6,000 lines of Kanye West.
Beat: https://youtu.be/Vt8OM41TytU (modified - I took out some of the quiet parts so it was a more constant beat)
This video is a recording of "SpeechTest.py"
By Robbie Barrat, 2017: RobbieBarrat@gmail.com
The following is part of the output of "DeepRap.py"
----------------------------------------------------
To all my guys
You messin up my eyes
Fuck up your sleeves
And fire up the skies
...
They steal your whole sound, that's a Real WorldChallenge
You an addict of cheating but I took the charge
Stop running up my money tall, height advantage
Ohhhhh, ohhhh; ohhh you too much to handle in one package
Not now but right now, you know... I had to go downtown
Shorty come and fix you up like bobby brown
Then he help me get my back up in this part of town
I can make you come and fix you up like bobby brown
Driving in the sky, you got a new crib to hold down
Shit's about to go and put my mack berry down
...
So dangerous, but it's all terrific
I done wore designers I won't get specific
It seem like fine hoes is hard to get some traffic
He claim her heart colder than the music
Man I wish I had to play in traffic
You know when I heard it's the way we was magic
When see me with a spit like a paraplegic
Wonder why it so hard not to play in traffic
Really Doe got a hell of a romantic
Lifestyles of the egotistic
...
The Delorean parked in front of that wack shit
The doctor don't wanna hear that official shit
I'm in pajamas but I just want my credit
If this party ain't got it I'm killing this shit
There's only one girl on my Brad Pitt
Keep it up some god damn it I'm killing this shit
And I'm feeling smooth as the chorus hit
My homie called me like I owe her shit
But I need a girl who barely bra fit
Me and Common is back on my Brad Pitt
Man, you don't get caught up with stupid shit
Then she cut from a girl who barely bra fit
How it feel to wake up with on some silly shit
I wish you could get is a whole city lit
That's where I see they got it I'm killing this shit
Yeah you got a Benz and they ain't done shit
Reality is catching up with stupid shit
Maybe that'll work when you check, they ain't done shit
...
I use it as always nigga, as always
Made her sit in that motherfucker sideways
Oh girl your silhouette make me pull the toys
I wonder where they want her to Ruby TuesdaysTell me what we at a ring from Tiffany's
Yeah, tonight I see you in my home gym
When we die, the money should've changed him
I'm on TV talking like it's all in my home gym
And see Penthouse 3 where a nigga from my team
He nicknamed me K-Rock so they abbreviate him
I don't even want to talk some sense in him
Welcome to the most and they ain't expect from him
The next generation will be assaulting him
...

published:06 Feb 2017

views:99629

Video shows what neural network means. A real or virtual computer system designed to emulate the brain in its ability to "learn" to assess imprecise data.. Any network of neurons etc that function together to achieve a common purpose.. neural network synonyms: neural net, artificial neural network, simulated neural network. Neural network Meaning. How to pronounce, definition audio dictionary. How to say neural network. Powered by MaryTTS, Wiktionary

For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.

Deep learning

Deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations.

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervisedfeature learning and hierarchical feature extraction.

Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.

Learning

Learning is the act of acquiring new, or modifying and reinforcing, existing knowledge, behaviors, skills, values, or preferences and may involve synthesizing different types of information. The ability to learn is possessed by humans, animals, plants and some machines. Progress over time tends to follow a learning curve. It does not happen all at once, but builds upon and is shaped by previous knowledge. To that end, learning may be viewed as a process, rather than a collection of factual and procedural knowledge. Learning produces changes in the organism and the changes produced are relatively permanent.

This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. MotivationBehind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learni...Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

13:01

Neural network tutorial: The back-propagation algorithm (Part 1)

Neural network tutorial: The back-propagation algorithm (Part 1)

Neural network tutorial: The back-propagation algorithm (Part 1)

In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but the derivation is the same, and easily extensible.
Helpful diagram: https://www.dropbox.com/s/vj0qg9jlmy3mwof/Explanation_1.pdf?dl=0
This particular video goes from the derivative of the sigmoid itself to the delta for the output layer
The presentation can be found here: https://www.dropbox.com/s/z5bz0cw0boxxon1/BackPropagation.pdf?dl=0

4:45

Introduction to Deep Learning: What Are Convolutional Neural Networks?

Introduction to Deep Learning: What Are Convolutional Neural Networks?

Introduction to Deep Learning: What Are Convolutional Neural Networks?

Get free deep learning resources: https://goo.gl/Z6vLDU
Explore the basics behind convolutional neural networks (CNNs) in this MATLAB® Tech Talk. Broadly, CNNs are a common deep learning architecture – but what exactly is a CNN? This video breaks down this sometimes complicated concept into easy-to-understand parts. You’ll learn about 3 concepts: local receptive fields, shared weights and biases, and activation and pooling.
The video pulls together these three concepts and shows you how to configure the layers in a CNN.
You’ll also learn about the 3 ways to train CNNs for image analysis. These include: 1.) Training the model from scratch; 2.) Using transfer learning (based on the idea that you can use knowledge of one type of problem to solve a similar problem); 3.) Using a pretrained CNN to extract features for training a machine learning model.

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

5:39

How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)

How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)

How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is female, it is technically referred to as a lioness. Since this model does not take all necessary features into account while performing classification, it is said to underfit the data. The problem of underfitting is solved by adding more detail to the model to ensure that it properly captures the differences between classes.
On the other hand, a model may also consider every possible detail and develop very specific, complex rules for classification. For example, if one data point represents a lion that is 3.5 feet tall, weighs 305 pounds, and has 2.9-inch claws, the model may develop a rule that classifies every 3.5 foot tall, 305 pound cat with 2.9-inch claws as a lion. Such rules will accurately classify the training data, but will poorly generalize to new data samples. A model that develops these kinds of rules is said to overfit the data. In other words, the model has failed to identify the true patterns that differentiate the classes. As a separate example, if the data only contained tigers that grew up in a zoo, the model may have difficulty classifying tigers that grew up in the wild. So while improving the process of data collection is helpful to prevent this problem, the model must be designed to identify the most important patterns that identify a class, so that new samples can be properly classified.
With regards to neural networks, overfitting typically stems from too many input features, or the use of an overly-complicated network configuration. If the input count is too large, the training process may start to assign weights to features that either aren't needed or add unnecessary complexity to the model. An overly-complicated configuration may lead to the development of specific rules that improperly relate many different features, resulting in poor generalization.
Overfitting is a common problem in data science. One popular method to reduce overfitting is the use of a cross-validation data set along with parameter averaging. For neural networks, a common method is regularization. There are different types such as L1 and L2, but each of these follows the same principle – penalize the model for letting weights and biases become too large. Another method is MaxNorm constraints, which directly adds a size limit to the weights and biases. A different approach is dropout, which randomly switches off certain neurons in the network, preventing the model from becoming too dependent on a set of neurons and the associated weights and biases. While these methods are broadly applied across the model rather than used for systematically searching for problem patterns, they have been proven to reduce and sometimes prevent the problem of overfitting.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

3:26

Neural Network Written Rap Song

Neural Network Written Rap Song

Neural Network Written Rap Song

Excluding the beat; this song was written 100% by a deep neural network (if you aren't familiar with neural networks, just think artificial intelligence).
Source Code: https://github.com/robbiebarrat/rapping-neural-network
Lyrics: I wrote a neural network, which wrote this after being introduced to ~6,000 lines of Kanye West.
Beat: https://youtu.be/Vt8OM41TytU (modified - I took out some of the quiet parts so it was a more constant beat)
This video is a recording of "SpeechTest.py"
By Robbie Barrat, 2017: RobbieBarrat@gmail.com
The following is part of the output of "DeepRap.py"
----------------------------------------------------
To all my guys
You messin up my eyes
Fuck up your sleeves
And fire up the skies
...
They steal your whole sound, that's a Real WorldChallenge
You an addict of cheating but I took the charge
Stop running up my money tall, height advantage
Ohhhhh, ohhhh; ohhh you too much to handle in one package
Not now but right now, you know... I had to go downtown
Shorty come and fix you up like bobby brown
Then he help me get my back up in this part of town
I can make you come and fix you up like bobby brown
Driving in the sky, you got a new crib to hold down
Shit's about to go and put my mack berry down
...
So dangerous, but it's all terrific
I done wore designers I won't get specific
It seem like fine hoes is hard to get some traffic
He claim her heart colder than the music
Man I wish I had to play in traffic
You know when I heard it's the way we was magic
When see me with a spit like a paraplegic
Wonder why it so hard not to play in traffic
Really Doe got a hell of a romantic
Lifestyles of the egotistic
...
The Delorean parked in front of that wack shit
The doctor don't wanna hear that official shit
I'm in pajamas but I just want my credit
If this party ain't got it I'm killing this shit
There's only one girl on my Brad Pitt
Keep it up some god damn it I'm killing this shit
And I'm feeling smooth as the chorus hit
My homie called me like I owe her shit
But I need a girl who barely bra fit
Me and Common is back on my Brad Pitt
Man, you don't get caught up with stupid shit
Then she cut from a girl who barely bra fit
How it feel to wake up with on some silly shit
I wish you could get is a whole city lit
That's where I see they got it I'm killing this shit
Yeah you got a Benz and they ain't done shit
Reality is catching up with stupid shit
Maybe that'll work when you check, they ain't done shit
...
I use it as always nigga, as always
Made her sit in that motherfucker sideways
Oh girl your silhouette make me pull the toys
I wonder where they want her to Ruby TuesdaysTell me what we at a ring from Tiffany's
Yeah, tonight I see you in my home gym
When we die, the money should've changed him
I'm on TV talking like it's all in my home gym
And see Penthouse 3 where a nigga from my team
He nicknamed me K-Rock so they abbreviate him
I don't even want to talk some sense in him
Welcome to the most and they ain't expect from him
The next generation will be assaulting him
...

0:38

Neural network Meaning

Neural network Meaning

Neural network Meaning

Video shows what neural network means. A real or virtual computer system designed to emulate the brain in its ability to "learn" to assess imprecise data.. Any network of neurons etc that function together to achieve a common purpose.. neural network synonyms: neural net, artificial neural network, simulated neural network. Neural network Meaning. How to pronounce, definition audio dictionary. How to say neural network. Powered by MaryTTS, Wiktionary

Cadence Demonstration of a Neural Network on the Vision P6 DSP

For more information about embedded vision, including hundreds of additional videos, please visit http://www.embedded-vision.com.
MeghaDagha , Senior Technical Marketing Manager at Cadence, demonstrates the company's latest embedded vision technologies and products at the May 2017 EmbeddedVisionSummit. Specifically, Daga demonstrates Cadence’s highly programmable, efficient Tensilica® Vision P6 DSP by showing an implementation of one of the most common neural networks, AlexNet. The demo uses a trained floating-point neural network from Caffe. Tensilica provided the most optimal solution by using their custom quantization to 8 bits with less than 1% accuracy drop. By using quantized coefficients, the Vision P6 DSP was able to achieve low bandwidth, low power, and high performance at minimal degradation.

6:45

Topic 23 - Activating a relaxed state of mind

Topic 23 - Activating a relaxed state of mind

Topic 23 - Activating a relaxed state of mind

The mind and body share one common neural network. Irritation within your mind flows directly into your body and increases your sensitivity to pain and the tendency for muscle spasm. Dr Anthony A Blisko describes how to quiet your mind in order to experience a healthier state of ease within both your mind and body.

Neural network tutorial: The back-propagation algorithm (Part 1)

In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but the derivation is the same, and easily extensible.
Helpful diagram: https://www.dropbox.com/s/vj0qg9jlmy3mwof/Explanation_1.pdf?dl=0
This particular video goes from the derivative of the sigmoid itself to the delta for the output layer
The presentation can be found here: https://www.dropbox.com/s/z5bz0cw0boxxon1/BackPropagation.pdf?dl=0

published: 07 Jan 2012

Introduction to Deep Learning: What Are Convolutional Neural Networks?

Get free deep learning resources: https://goo.gl/Z6vLDU
Explore the basics behind convolutional neural networks (CNNs) in this MATLAB® Tech Talk. Broadly, CNNs are a common deep learning architecture – but what exactly is a CNN? This video breaks down this sometimes complicated concept into easy-to-understand parts. You’ll learn about 3 concepts: local receptive fields, shared weights and biases, and activation and pooling.
The video pulls together these three concepts and shows you how to configure the layers in a CNN.
You’ll also learn about the 3 ways to train CNNs for image analysis. These include: 1.) Training the model from scratch; 2.) Using transfer learning (based on the idea that you can use knowledge of one type of problem to solve a similar problem); 3.) Using a pretrained ...

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video update...

published: 01 Jun 2017

How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is fem...

published: 03 Jun 2016

Neural Network Written Rap Song

Excluding the beat; this song was written 100% by a deep neural network (if you aren't familiar with neural networks, just think artificial intelligence).
Source Code: https://github.com/robbiebarrat/rapping-neural-network
Lyrics: I wrote a neural network, which wrote this after being introduced to ~6,000 lines of Kanye West.
Beat: https://youtu.be/Vt8OM41TytU (modified - I took out some of the quiet parts so it was a more constant beat)
This video is a recording of "SpeechTest.py"
By Robbie Barrat, 2017: RobbieBarrat@gmail.com
The following is part of the output of "DeepRap.py"
----------------------------------------------------
To all my guys
You messin up my eyes
Fuck up your sleeves
And fire up the skies
...
They steal your whole sound, that's a Real WorldChallenge
You an add...

published: 06 Feb 2017

Neural network Meaning

Video shows what neural network means. A real or virtual computer system designed to emulate the brain in its ability to "learn" to assess imprecise data.. Any network of neurons etc that function together to achieve a common purpose.. neural network synonyms: neural net, artificial neural network, simulated neural network. Neural network Meaning. How to pronounce, definition audio dictionary. How to say neural network. Powered by MaryTTS, Wiktionary

Cadence Demonstration of a Neural Network on the Vision P6 DSP

For more information about embedded vision, including hundreds of additional videos, please visit http://www.embedded-vision.com.
MeghaDagha , Senior Technical Marketing Manager at Cadence, demonstrates the company's latest embedded vision technologies and products at the May 2017 EmbeddedVisionSummit. Specifically, Daga demonstrates Cadence’s highly programmable, efficient Tensilica® Vision P6 DSP by showing an implementation of one of the most common neural networks, AlexNet. The demo uses a trained floating-point neural network from Caffe. Tensilica provided the most optimal solution by using their custom quantization to 8 bits with less than 1% accuracy drop. By using quantized coefficients, the Vision P6 DSP was able to achieve low bandwidth, low power, and high performance at min...

published: 10 Jun 2017

Topic 23 - Activating a relaxed state of mind

The mind and body share one common neural network. Irritation within your mind flows directly into your body and increases your sensitivity to pain and the tendency for muscle spasm. Dr Anthony A Blisko describes how to quiet your mind in order to experience a healthier state of ease within both your mind and body.

This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep ...

This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. MotivationBehind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learni...Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. MotivationBehind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learni...Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Neural network tutorial: The back-propagation algorithm (Part 1)

In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, b...

In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but the derivation is the same, and easily extensible.
Helpful diagram: https://www.dropbox.com/s/vj0qg9jlmy3mwof/Explanation_1.pdf?dl=0
This particular video goes from the derivative of the sigmoid itself to the delta for the output layer
The presentation can be found here: https://www.dropbox.com/s/z5bz0cw0boxxon1/BackPropagation.pdf?dl=0

In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but the derivation is the same, and easily extensible.
Helpful diagram: https://www.dropbox.com/s/vj0qg9jlmy3mwof/Explanation_1.pdf?dl=0
This particular video goes from the derivative of the sigmoid itself to the delta for the output layer
The presentation can be found here: https://www.dropbox.com/s/z5bz0cw0boxxon1/BackPropagation.pdf?dl=0

published:07 Jan 2012

views:221941

back

Introduction to Deep Learning: What Are Convolutional Neural Networks?

Get free deep learning resources: https://goo.gl/Z6vLDU
Explore the basics behind convolutional neural networks (CNNs) in this MATLAB® Tech Talk. Broadly, CNNs are a common deep learning architecture – but what exactly is a CNN? This video breaks down this sometimes complicated concept into easy-to-understand parts. You’ll learn about 3 concepts: local receptive fields, shared weights and biases, and activation and pooling.
The video pulls together these three concepts and shows you how to configure the layers in a CNN.
You’ll also learn about the 3 ways to train CNNs for image analysis. These include: 1.) Training the model from scratch; 2.) Using transfer learning (based on the idea that you can use knowledge of one type of problem to solve a similar problem); 3.) Using a pretrained CNN to extract features for training a machine learning model.

Get free deep learning resources: https://goo.gl/Z6vLDU
Explore the basics behind convolutional neural networks (CNNs) in this MATLAB® Tech Talk. Broadly, CNNs are a common deep learning architecture – but what exactly is a CNN? This video breaks down this sometimes complicated concept into easy-to-understand parts. You’ll learn about 3 concepts: local receptive fields, shared weights and biases, and activation and pooling.
The video pulls together these three concepts and shows you how to configure the layers in a CNN.
You’ll also learn about the 3 ways to train CNNs for image analysis. These include: 1.) Training the model from scratch; 2.) Using transfer learning (based on the idea that you can use knowledge of one type of problem to solve a similar problem); 3.) Using a pretrained CNN to extract features for training a machine learning model.

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examp...

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will...

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is female, it is technically referred to as a lioness. Since this model does not take all necessary features into account while performing classification, it is said to underfit the data. The problem of underfitting is solved by adding more detail to the model to ensure that it properly captures the differences between classes.
On the other hand, a model may also consider every possible detail and develop very specific, complex rules for classification. For example, if one data point represents a lion that is 3.5 feet tall, weighs 305 pounds, and has 2.9-inch claws, the model may develop a rule that classifies every 3.5 foot tall, 305 pound cat with 2.9-inch claws as a lion. Such rules will accurately classify the training data, but will poorly generalize to new data samples. A model that develops these kinds of rules is said to overfit the data. In other words, the model has failed to identify the true patterns that differentiate the classes. As a separate example, if the data only contained tigers that grew up in a zoo, the model may have difficulty classifying tigers that grew up in the wild. So while improving the process of data collection is helpful to prevent this problem, the model must be designed to identify the most important patterns that identify a class, so that new samples can be properly classified.
With regards to neural networks, overfitting typically stems from too many input features, or the use of an overly-complicated network configuration. If the input count is too large, the training process may start to assign weights to features that either aren't needed or add unnecessary complexity to the model. An overly-complicated configuration may lead to the development of specific rules that improperly relate many different features, resulting in poor generalization.
Overfitting is a common problem in data science. One popular method to reduce overfitting is the use of a cross-validation data set along with parameter averaging. For neural networks, a common method is regularization. There are different types such as L1 and L2, but each of these follows the same principle – penalize the model for letting weights and biases become too large. Another method is MaxNorm constraints, which directly adds a size limit to the weights and biases. A different approach is dropout, which randomly switches off certain neurons in the network, preventing the model from becoming too dependent on a set of neurons and the associated weights and biases. While these methods are broadly applied across the model rather than used for systematically searching for problem patterns, they have been proven to reduce and sometimes prevent the problem of overfitting.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is female, it is technically referred to as a lioness. Since this model does not take all necessary features into account while performing classification, it is said to underfit the data. The problem of underfitting is solved by adding more detail to the model to ensure that it properly captures the differences between classes.
On the other hand, a model may also consider every possible detail and develop very specific, complex rules for classification. For example, if one data point represents a lion that is 3.5 feet tall, weighs 305 pounds, and has 2.9-inch claws, the model may develop a rule that classifies every 3.5 foot tall, 305 pound cat with 2.9-inch claws as a lion. Such rules will accurately classify the training data, but will poorly generalize to new data samples. A model that develops these kinds of rules is said to overfit the data. In other words, the model has failed to identify the true patterns that differentiate the classes. As a separate example, if the data only contained tigers that grew up in a zoo, the model may have difficulty classifying tigers that grew up in the wild. So while improving the process of data collection is helpful to prevent this problem, the model must be designed to identify the most important patterns that identify a class, so that new samples can be properly classified.
With regards to neural networks, overfitting typically stems from too many input features, or the use of an overly-complicated network configuration. If the input count is too large, the training process may start to assign weights to features that either aren't needed or add unnecessary complexity to the model. An overly-complicated configuration may lead to the development of specific rules that improperly relate many different features, resulting in poor generalization.
Overfitting is a common problem in data science. One popular method to reduce overfitting is the use of a cross-validation data set along with parameter averaging. For neural networks, a common method is regularization. There are different types such as L1 and L2, but each of these follows the same principle – penalize the model for letting weights and biases become too large. Another method is MaxNorm constraints, which directly adds a size limit to the weights and biases. A different approach is dropout, which randomly switches off certain neurons in the network, preventing the model from becoming too dependent on a set of neurons and the associated weights and biases. While these methods are broadly applied across the model rather than used for systematically searching for problem patterns, they have been proven to reduce and sometimes prevent the problem of overfitting.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Neural Network Written Rap Song

Excluding the beat; this song was written 100% by a deep neural network (if you aren't familiar with neural networks, just think artificial intelligence).
Sour...

Excluding the beat; this song was written 100% by a deep neural network (if you aren't familiar with neural networks, just think artificial intelligence).
Source Code: https://github.com/robbiebarrat/rapping-neural-network
Lyrics: I wrote a neural network, which wrote this after being introduced to ~6,000 lines of Kanye West.
Beat: https://youtu.be/Vt8OM41TytU (modified - I took out some of the quiet parts so it was a more constant beat)
This video is a recording of "SpeechTest.py"
By Robbie Barrat, 2017: RobbieBarrat@gmail.com
The following is part of the output of "DeepRap.py"
----------------------------------------------------
To all my guys
You messin up my eyes
Fuck up your sleeves
And fire up the skies
...
They steal your whole sound, that's a Real WorldChallenge
You an addict of cheating but I took the charge
Stop running up my money tall, height advantage
Ohhhhh, ohhhh; ohhh you too much to handle in one package
Not now but right now, you know... I had to go downtown
Shorty come and fix you up like bobby brown
Then he help me get my back up in this part of town
I can make you come and fix you up like bobby brown
Driving in the sky, you got a new crib to hold down
Shit's about to go and put my mack berry down
...
So dangerous, but it's all terrific
I done wore designers I won't get specific
It seem like fine hoes is hard to get some traffic
He claim her heart colder than the music
Man I wish I had to play in traffic
You know when I heard it's the way we was magic
When see me with a spit like a paraplegic
Wonder why it so hard not to play in traffic
Really Doe got a hell of a romantic
Lifestyles of the egotistic
...
The Delorean parked in front of that wack shit
The doctor don't wanna hear that official shit
I'm in pajamas but I just want my credit
If this party ain't got it I'm killing this shit
There's only one girl on my Brad Pitt
Keep it up some god damn it I'm killing this shit
And I'm feeling smooth as the chorus hit
My homie called me like I owe her shit
But I need a girl who barely bra fit
Me and Common is back on my Brad Pitt
Man, you don't get caught up with stupid shit
Then she cut from a girl who barely bra fit
How it feel to wake up with on some silly shit
I wish you could get is a whole city lit
That's where I see they got it I'm killing this shit
Yeah you got a Benz and they ain't done shit
Reality is catching up with stupid shit
Maybe that'll work when you check, they ain't done shit
...
I use it as always nigga, as always
Made her sit in that motherfucker sideways
Oh girl your silhouette make me pull the toys
I wonder where they want her to Ruby TuesdaysTell me what we at a ring from Tiffany's
Yeah, tonight I see you in my home gym
When we die, the money should've changed him
I'm on TV talking like it's all in my home gym
And see Penthouse 3 where a nigga from my team
He nicknamed me K-Rock so they abbreviate him
I don't even want to talk some sense in him
Welcome to the most and they ain't expect from him
The next generation will be assaulting him
...

Excluding the beat; this song was written 100% by a deep neural network (if you aren't familiar with neural networks, just think artificial intelligence).
Source Code: https://github.com/robbiebarrat/rapping-neural-network
Lyrics: I wrote a neural network, which wrote this after being introduced to ~6,000 lines of Kanye West.
Beat: https://youtu.be/Vt8OM41TytU (modified - I took out some of the quiet parts so it was a more constant beat)
This video is a recording of "SpeechTest.py"
By Robbie Barrat, 2017: RobbieBarrat@gmail.com
The following is part of the output of "DeepRap.py"
----------------------------------------------------
To all my guys
You messin up my eyes
Fuck up your sleeves
And fire up the skies
...
They steal your whole sound, that's a Real WorldChallenge
You an addict of cheating but I took the charge
Stop running up my money tall, height advantage
Ohhhhh, ohhhh; ohhh you too much to handle in one package
Not now but right now, you know... I had to go downtown
Shorty come and fix you up like bobby brown
Then he help me get my back up in this part of town
I can make you come and fix you up like bobby brown
Driving in the sky, you got a new crib to hold down
Shit's about to go and put my mack berry down
...
So dangerous, but it's all terrific
I done wore designers I won't get specific
It seem like fine hoes is hard to get some traffic
He claim her heart colder than the music
Man I wish I had to play in traffic
You know when I heard it's the way we was magic
When see me with a spit like a paraplegic
Wonder why it so hard not to play in traffic
Really Doe got a hell of a romantic
Lifestyles of the egotistic
...
The Delorean parked in front of that wack shit
The doctor don't wanna hear that official shit
I'm in pajamas but I just want my credit
If this party ain't got it I'm killing this shit
There's only one girl on my Brad Pitt
Keep it up some god damn it I'm killing this shit
And I'm feeling smooth as the chorus hit
My homie called me like I owe her shit
But I need a girl who barely bra fit
Me and Common is back on my Brad Pitt
Man, you don't get caught up with stupid shit
Then she cut from a girl who barely bra fit
How it feel to wake up with on some silly shit
I wish you could get is a whole city lit
That's where I see they got it I'm killing this shit
Yeah you got a Benz and they ain't done shit
Reality is catching up with stupid shit
Maybe that'll work when you check, they ain't done shit
...
I use it as always nigga, as always
Made her sit in that motherfucker sideways
Oh girl your silhouette make me pull the toys
I wonder where they want her to Ruby TuesdaysTell me what we at a ring from Tiffany's
Yeah, tonight I see you in my home gym
When we die, the money should've changed him
I'm on TV talking like it's all in my home gym
And see Penthouse 3 where a nigga from my team
He nicknamed me K-Rock so they abbreviate him
I don't even want to talk some sense in him
Welcome to the most and they ain't expect from him
The next generation will be assaulting him
...

Neural network Meaning

Video shows what neural network means. A real or virtual computer system designed to emulate the brain in its ability to "learn" to assess imprecise data.. Any ...

Video shows what neural network means. A real or virtual computer system designed to emulate the brain in its ability to "learn" to assess imprecise data.. Any network of neurons etc that function together to achieve a common purpose.. neural network synonyms: neural net, artificial neural network, simulated neural network. Neural network Meaning. How to pronounce, definition audio dictionary. How to say neural network. Powered by MaryTTS, Wiktionary

Video shows what neural network means. A real or virtual computer system designed to emulate the brain in its ability to "learn" to assess imprecise data.. Any network of neurons etc that function together to achieve a common purpose.. neural network synonyms: neural net, artificial neural network, simulated neural network. Neural network Meaning. How to pronounce, definition audio dictionary. How to say neural network. Powered by MaryTTS, Wiktionary

For more information about embedded vision, including hundreds of additional videos, please visit http://www.embedded-vision.com.
MeghaDagha , Senior Technical Marketing Manager at Cadence, demonstrates the company's latest embedded vision technologies and products at the May 2017 EmbeddedVisionSummit. Specifically, Daga demonstrates Cadence’s highly programmable, efficient Tensilica® Vision P6 DSP by showing an implementation of one of the most common neural networks, AlexNet. The demo uses a trained floating-point neural network from Caffe. Tensilica provided the most optimal solution by using their custom quantization to 8 bits with less than 1% accuracy drop. By using quantized coefficients, the Vision P6 DSP was able to achieve low bandwidth, low power, and high performance at minimal degradation.

For more information about embedded vision, including hundreds of additional videos, please visit http://www.embedded-vision.com.
MeghaDagha , Senior Technical Marketing Manager at Cadence, demonstrates the company's latest embedded vision technologies and products at the May 2017 EmbeddedVisionSummit. Specifically, Daga demonstrates Cadence’s highly programmable, efficient Tensilica® Vision P6 DSP by showing an implementation of one of the most common neural networks, AlexNet. The demo uses a trained floating-point neural network from Caffe. Tensilica provided the most optimal solution by using their custom quantization to 8 bits with less than 1% accuracy drop. By using quantized coefficients, the Vision P6 DSP was able to achieve low bandwidth, low power, and high performance at minimal degradation.

Topic 23 - Activating a relaxed state of mind

The mind and body share one common neural network. Irritation within your mind flows directly into your body and increases your sensitivity to pain and the tend...

The mind and body share one common neural network. Irritation within your mind flows directly into your body and increases your sensitivity to pain and the tendency for muscle spasm. Dr Anthony A Blisko describes how to quiet your mind in order to experience a healthier state of ease within both your mind and body.

The mind and body share one common neural network. Irritation within your mind flows directly into your body and increases your sensitivity to pain and the tendency for muscle spasm. Dr Anthony A Blisko describes how to quiet your mind in order to experience a healthier state of ease within both your mind and body.

Neural network tutorial: The back-propagation algorithm (Part 1)

In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but the derivation is the same, and easily extensible.
Helpful diagram: https://www.dropbox.com/s/vj0qg9jlmy3mwof/Explanation_1.pdf?dl=0
This particular video goes from the derivative of the sigmoid itself to the delta for the output layer
The presentation can be found here: https://www.dropbox.com/s/z5bz0cw0boxxon1/BackPropagation.pdf?dl=0

published: 07 Jan 2012

Introduction to Deep Learning: What Are Convolutional Neural Networks?

Get free deep learning resources: https://goo.gl/Z6vLDU
Explore the basics behind convolutional neural networks (CNNs) in this MATLAB® Tech Talk. Broadly, CNNs are a common deep learning architecture – but what exactly is a CNN? This video breaks down this sometimes complicated concept into easy-to-understand parts. You’ll learn about 3 concepts: local receptive fields, shared weights and biases, and activation and pooling.
The video pulls together these three concepts and shows you how to configure the layers in a CNN.
You’ll also learn about the 3 ways to train CNNs for image analysis. These include: 1.) Training the model from scratch; 2.) Using transfer learning (based on the idea that you can use knowledge of one type of problem to solve a similar problem); 3.) Using a pretrained ...

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video update...

published: 01 Jun 2017

How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is fem...

published: 03 Jun 2016

Neural Network Written Rap Song

Excluding the beat; this song was written 100% by a deep neural network (if you aren't familiar with neural networks, just think artificial intelligence).
Source Code: https://github.com/robbiebarrat/rapping-neural-network
Lyrics: I wrote a neural network, which wrote this after being introduced to ~6,000 lines of Kanye West.
Beat: https://youtu.be/Vt8OM41TytU (modified - I took out some of the quiet parts so it was a more constant beat)
This video is a recording of "SpeechTest.py"
By Robbie Barrat, 2017: RobbieBarrat@gmail.com
The following is part of the output of "DeepRap.py"
----------------------------------------------------
To all my guys
You messin up my eyes
Fuck up your sleeves
And fire up the skies
...
They steal your whole sound, that's a Real WorldChallenge
You an add...

published: 06 Feb 2017

Neural network Meaning

Video shows what neural network means. A real or virtual computer system designed to emulate the brain in its ability to "learn" to assess imprecise data.. Any network of neurons etc that function together to achieve a common purpose.. neural network synonyms: neural net, artificial neural network, simulated neural network. Neural network Meaning. How to pronounce, definition audio dictionary. How to say neural network. Powered by MaryTTS, Wiktionary

This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep ...

This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. MotivationBehind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learni...Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. MotivationBehind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learni...Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Neural network tutorial: The back-propagation algorithm (Part 1)

In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, b...

In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but the derivation is the same, and easily extensible.
Helpful diagram: https://www.dropbox.com/s/vj0qg9jlmy3mwof/Explanation_1.pdf?dl=0
This particular video goes from the derivative of the sigmoid itself to the delta for the output layer
The presentation can be found here: https://www.dropbox.com/s/z5bz0cw0boxxon1/BackPropagation.pdf?dl=0

In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but the derivation is the same, and easily extensible.
Helpful diagram: https://www.dropbox.com/s/vj0qg9jlmy3mwof/Explanation_1.pdf?dl=0
This particular video goes from the derivative of the sigmoid itself to the delta for the output layer
The presentation can be found here: https://www.dropbox.com/s/z5bz0cw0boxxon1/BackPropagation.pdf?dl=0

published:07 Jan 2012

views:221941

back

Introduction to Deep Learning: What Are Convolutional Neural Networks?

Get free deep learning resources: https://goo.gl/Z6vLDU
Explore the basics behind convolutional neural networks (CNNs) in this MATLAB® Tech Talk. Broadly, CNNs are a common deep learning architecture – but what exactly is a CNN? This video breaks down this sometimes complicated concept into easy-to-understand parts. You’ll learn about 3 concepts: local receptive fields, shared weights and biases, and activation and pooling.
The video pulls together these three concepts and shows you how to configure the layers in a CNN.
You’ll also learn about the 3 ways to train CNNs for image analysis. These include: 1.) Training the model from scratch; 2.) Using transfer learning (based on the idea that you can use knowledge of one type of problem to solve a similar problem); 3.) Using a pretrained CNN to extract features for training a machine learning model.

Get free deep learning resources: https://goo.gl/Z6vLDU
Explore the basics behind convolutional neural networks (CNNs) in this MATLAB® Tech Talk. Broadly, CNNs are a common deep learning architecture – but what exactly is a CNN? This video breaks down this sometimes complicated concept into easy-to-understand parts. You’ll learn about 3 concepts: local receptive fields, shared weights and biases, and activation and pooling.
The video pulls together these three concepts and shows you how to configure the layers in a CNN.
You’ll also learn about the 3 ways to train CNNs for image analysis. These include: 1.) Training the model from scratch; 2.) Using transfer learning (based on the idea that you can use knowledge of one type of problem to solve a similar problem); 3.) Using a pretrained CNN to extract features for training a machine learning model.

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examp...

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will...

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is female, it is technically referred to as a lioness. Since this model does not take all necessary features into account while performing classification, it is said to underfit the data. The problem of underfitting is solved by adding more detail to the model to ensure that it properly captures the differences between classes.
On the other hand, a model may also consider every possible detail and develop very specific, complex rules for classification. For example, if one data point represents a lion that is 3.5 feet tall, weighs 305 pounds, and has 2.9-inch claws, the model may develop a rule that classifies every 3.5 foot tall, 305 pound cat with 2.9-inch claws as a lion. Such rules will accurately classify the training data, but will poorly generalize to new data samples. A model that develops these kinds of rules is said to overfit the data. In other words, the model has failed to identify the true patterns that differentiate the classes. As a separate example, if the data only contained tigers that grew up in a zoo, the model may have difficulty classifying tigers that grew up in the wild. So while improving the process of data collection is helpful to prevent this problem, the model must be designed to identify the most important patterns that identify a class, so that new samples can be properly classified.
With regards to neural networks, overfitting typically stems from too many input features, or the use of an overly-complicated network configuration. If the input count is too large, the training process may start to assign weights to features that either aren't needed or add unnecessary complexity to the model. An overly-complicated configuration may lead to the development of specific rules that improperly relate many different features, resulting in poor generalization.
Overfitting is a common problem in data science. One popular method to reduce overfitting is the use of a cross-validation data set along with parameter averaging. For neural networks, a common method is regularization. There are different types such as L1 and L2, but each of these follows the same principle – penalize the model for letting weights and biases become too large. Another method is MaxNorm constraints, which directly adds a size limit to the weights and biases. A different approach is dropout, which randomly switches off certain neurons in the network, preventing the model from becoming too dependent on a set of neurons and the associated weights and biases. While these methods are broadly applied across the model rather than used for systematically searching for problem patterns, they have been proven to reduce and sometimes prevent the problem of overfitting.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is female, it is technically referred to as a lioness. Since this model does not take all necessary features into account while performing classification, it is said to underfit the data. The problem of underfitting is solved by adding more detail to the model to ensure that it properly captures the differences between classes.
On the other hand, a model may also consider every possible detail and develop very specific, complex rules for classification. For example, if one data point represents a lion that is 3.5 feet tall, weighs 305 pounds, and has 2.9-inch claws, the model may develop a rule that classifies every 3.5 foot tall, 305 pound cat with 2.9-inch claws as a lion. Such rules will accurately classify the training data, but will poorly generalize to new data samples. A model that develops these kinds of rules is said to overfit the data. In other words, the model has failed to identify the true patterns that differentiate the classes. As a separate example, if the data only contained tigers that grew up in a zoo, the model may have difficulty classifying tigers that grew up in the wild. So while improving the process of data collection is helpful to prevent this problem, the model must be designed to identify the most important patterns that identify a class, so that new samples can be properly classified.
With regards to neural networks, overfitting typically stems from too many input features, or the use of an overly-complicated network configuration. If the input count is too large, the training process may start to assign weights to features that either aren't needed or add unnecessary complexity to the model. An overly-complicated configuration may lead to the development of specific rules that improperly relate many different features, resulting in poor generalization.
Overfitting is a common problem in data science. One popular method to reduce overfitting is the use of a cross-validation data set along with parameter averaging. For neural networks, a common method is regularization. There are different types such as L1 and L2, but each of these follows the same principle – penalize the model for letting weights and biases become too large. Another method is MaxNorm constraints, which directly adds a size limit to the weights and biases. A different approach is dropout, which randomly switches off certain neurons in the network, preventing the model from becoming too dependent on a set of neurons and the associated weights and biases. While these methods are broadly applied across the model rather than used for systematically searching for problem patterns, they have been proven to reduce and sometimes prevent the problem of overfitting.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Neural Network Written Rap Song

Excluding the beat; this song was written 100% by a deep neural network (if you aren't familiar with neural networks, just think artificial intelligence).
Sour...

Excluding the beat; this song was written 100% by a deep neural network (if you aren't familiar with neural networks, just think artificial intelligence).
Source Code: https://github.com/robbiebarrat/rapping-neural-network
Lyrics: I wrote a neural network, which wrote this after being introduced to ~6,000 lines of Kanye West.
Beat: https://youtu.be/Vt8OM41TytU (modified - I took out some of the quiet parts so it was a more constant beat)
This video is a recording of "SpeechTest.py"
By Robbie Barrat, 2017: RobbieBarrat@gmail.com
The following is part of the output of "DeepRap.py"
----------------------------------------------------
To all my guys
You messin up my eyes
Fuck up your sleeves
And fire up the skies
...
They steal your whole sound, that's a Real WorldChallenge
You an addict of cheating but I took the charge
Stop running up my money tall, height advantage
Ohhhhh, ohhhh; ohhh you too much to handle in one package
Not now but right now, you know... I had to go downtown
Shorty come and fix you up like bobby brown
Then he help me get my back up in this part of town
I can make you come and fix you up like bobby brown
Driving in the sky, you got a new crib to hold down
Shit's about to go and put my mack berry down
...
So dangerous, but it's all terrific
I done wore designers I won't get specific
It seem like fine hoes is hard to get some traffic
He claim her heart colder than the music
Man I wish I had to play in traffic
You know when I heard it's the way we was magic
When see me with a spit like a paraplegic
Wonder why it so hard not to play in traffic
Really Doe got a hell of a romantic
Lifestyles of the egotistic
...
The Delorean parked in front of that wack shit
The doctor don't wanna hear that official shit
I'm in pajamas but I just want my credit
If this party ain't got it I'm killing this shit
There's only one girl on my Brad Pitt
Keep it up some god damn it I'm killing this shit
And I'm feeling smooth as the chorus hit
My homie called me like I owe her shit
But I need a girl who barely bra fit
Me and Common is back on my Brad Pitt
Man, you don't get caught up with stupid shit
Then she cut from a girl who barely bra fit
How it feel to wake up with on some silly shit
I wish you could get is a whole city lit
That's where I see they got it I'm killing this shit
Yeah you got a Benz and they ain't done shit
Reality is catching up with stupid shit
Maybe that'll work when you check, they ain't done shit
...
I use it as always nigga, as always
Made her sit in that motherfucker sideways
Oh girl your silhouette make me pull the toys
I wonder where they want her to Ruby TuesdaysTell me what we at a ring from Tiffany's
Yeah, tonight I see you in my home gym
When we die, the money should've changed him
I'm on TV talking like it's all in my home gym
And see Penthouse 3 where a nigga from my team
He nicknamed me K-Rock so they abbreviate him
I don't even want to talk some sense in him
Welcome to the most and they ain't expect from him
The next generation will be assaulting him
...

Excluding the beat; this song was written 100% by a deep neural network (if you aren't familiar with neural networks, just think artificial intelligence).
Source Code: https://github.com/robbiebarrat/rapping-neural-network
Lyrics: I wrote a neural network, which wrote this after being introduced to ~6,000 lines of Kanye West.
Beat: https://youtu.be/Vt8OM41TytU (modified - I took out some of the quiet parts so it was a more constant beat)
This video is a recording of "SpeechTest.py"
By Robbie Barrat, 2017: RobbieBarrat@gmail.com
The following is part of the output of "DeepRap.py"
----------------------------------------------------
To all my guys
You messin up my eyes
Fuck up your sleeves
And fire up the skies
...
They steal your whole sound, that's a Real WorldChallenge
You an addict of cheating but I took the charge
Stop running up my money tall, height advantage
Ohhhhh, ohhhh; ohhh you too much to handle in one package
Not now but right now, you know... I had to go downtown
Shorty come and fix you up like bobby brown
Then he help me get my back up in this part of town
I can make you come and fix you up like bobby brown
Driving in the sky, you got a new crib to hold down
Shit's about to go and put my mack berry down
...
So dangerous, but it's all terrific
I done wore designers I won't get specific
It seem like fine hoes is hard to get some traffic
He claim her heart colder than the music
Man I wish I had to play in traffic
You know when I heard it's the way we was magic
When see me with a spit like a paraplegic
Wonder why it so hard not to play in traffic
Really Doe got a hell of a romantic
Lifestyles of the egotistic
...
The Delorean parked in front of that wack shit
The doctor don't wanna hear that official shit
I'm in pajamas but I just want my credit
If this party ain't got it I'm killing this shit
There's only one girl on my Brad Pitt
Keep it up some god damn it I'm killing this shit
And I'm feeling smooth as the chorus hit
My homie called me like I owe her shit
But I need a girl who barely bra fit
Me and Common is back on my Brad Pitt
Man, you don't get caught up with stupid shit
Then she cut from a girl who barely bra fit
How it feel to wake up with on some silly shit
I wish you could get is a whole city lit
That's where I see they got it I'm killing this shit
Yeah you got a Benz and they ain't done shit
Reality is catching up with stupid shit
Maybe that'll work when you check, they ain't done shit
...
I use it as always nigga, as always
Made her sit in that motherfucker sideways
Oh girl your silhouette make me pull the toys
I wonder where they want her to Ruby TuesdaysTell me what we at a ring from Tiffany's
Yeah, tonight I see you in my home gym
When we die, the money should've changed him
I'm on TV talking like it's all in my home gym
And see Penthouse 3 where a nigga from my team
He nicknamed me K-Rock so they abbreviate him
I don't even want to talk some sense in him
Welcome to the most and they ain't expect from him
The next generation will be assaulting him
...

Neural network Meaning

Video shows what neural network means. A real or virtual computer system designed to emulate the brain in its ability to "learn" to assess imprecise data.. Any ...

Video shows what neural network means. A real or virtual computer system designed to emulate the brain in its ability to "learn" to assess imprecise data.. Any network of neurons etc that function together to achieve a common purpose.. neural network synonyms: neural net, artificial neural network, simulated neural network. Neural network Meaning. How to pronounce, definition audio dictionary. How to say neural network. Powered by MaryTTS, Wiktionary

Video shows what neural network means. A real or virtual computer system designed to emulate the brain in its ability to "learn" to assess imprecise data.. Any network of neurons etc that function together to achieve a common purpose.. neural network synonyms: neural net, artificial neural network, simulated neural network. Neural network Meaning. How to pronounce, definition audio dictionary. How to say neural network. Powered by MaryTTS, Wiktionary

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video update...

Neural Net in C++ Tutorial on Vimeo

published: 09 Mar 2013

Tutorial on Convolutional Neural Networks(CNNs) for image recognition

This tutorial would help you understand Deep learning frameworks, such as convolutional neural networks (CNNs), which have almost completely replaced other machine learning techniques for specific tasks such as image recognition using large training datasets. In this webinar, we will go over how CNNs, their training methods, and hardware evolved since LeNet first appeared in the late 1990’s. We will examine the challenges that came along, and some key innovations that helped overcome these challenges. We will also look at a guide on how to get started with CNNs, some common pitfalls, and tips and tricks in training CNNs. Advanced Technology Group (ATG) of the CTOOffice at NetApp. The ATG group is responsible for investigations, through early product prototypes, and leveraging technologies...

This Edureka "What isDeep Learning" video (Blog: https://goo.gl/4zxMfU) will help you to understand about the relationship between Deep Learning, Machine Learning and Artificial Intelligence and how Deep Learning came into the picture. This tutorial will be discussing about Artificial Intelligence, Machine Learning and its limitations, how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning.
Below are the topics covered in this tutorial:
1. What Is Artificial Intelligence?
2. What Is Machine Learning?
3. Limitations Of Machine Learning
4. Deep Learning To The Rescue
5. What Is Deep Learning?
6. Deep Learning Applications
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning...

published: 10 May 2017

2nd-order Optimization for Neural Network Training

Neural networks have become the main workhorse of supervised learning, and their efficient training is an important technical challenge which has received a lot of attention. While stochastic gradient descent (SGD) with momentum works well enough in many situations, its performance declines dramatically as networks become deeper and more complex. Given their success in other domains, 2nd-order optimization methods seem like a promising alternative. Unfortunately, the cost of inverting the curvature matrix (traditionally the Hessian) is prohibitive for neural networks, due to their high dimension. One common solution is to approximate the curvature matrix as diagonal or low-rank. Because such approximations are quite crude, most of the theoretical power of 2nd-order methods is lost, an...

published: 11 Aug 2016

ConvNetJS – Deep Learning in your browser

ConvNetJS is a JavaScript library for training and running Convolutional Neural Networks in the browser. It can be used for common Machine Learning tasks, such as classification and regression and can get quite powerful using offline training. In this talk we will cover some basic theory, learn how and why to use CNNs in the browser and see some cool demos.
This great talk was held by ChristophKörner at the monthly ViennaJS meetup in Vienna: http://viennajs.org
The full slides from this talk can be found here: https://docs.google.com/presentation/d/1bLk23AF2lhH7t91vkW9HS-pYTYdNRriW-pjkC1GnK9E/

published: 04 Jun 2016

TensorFlow and Deep Learning without a PhD, Part 1 (Google Cloud Next '17)

With TensorFlow, deep machine learning transitions from an area of research to mainstream software engineering. In this video, Martin Gorner demonstrates how to construct and train a neural network that recognises handwritten digits. Along the way, he'll describe some "tricks of the trade" used in neural network design, and finally, he'll bring the recognition accuracy of his model above 99%.
Content applies to software developers of all levels. Experienced machine learning enthusiasts, this video will introduce you to TensorFlow through well known models such as dense and convolutional networks. This is an intense technical video designed to help beginners in machine learning ramp up quickly.
Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h
Watch more talks about ...

This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep ...

This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. MotivationBehind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learni...Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. MotivationBehind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learni...Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examp...

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Tutorial on Convolutional Neural Networks(CNNs) for image recognition

This tutorial would help you understand Deep learning frameworks, such as convolutional neural networks (CNNs), which have almost completely replaced other mach...

This tutorial would help you understand Deep learning frameworks, such as convolutional neural networks (CNNs), which have almost completely replaced other machine learning techniques for specific tasks such as image recognition using large training datasets. In this webinar, we will go over how CNNs, their training methods, and hardware evolved since LeNet first appeared in the late 1990’s. We will examine the challenges that came along, and some key innovations that helped overcome these challenges. We will also look at a guide on how to get started with CNNs, some common pitfalls, and tips and tricks in training CNNs. Advanced Technology Group (ATG) of the CTOOffice at NetApp. The ATG group is responsible for investigations, through early product prototypes, and leveraging technologies expected to become mainstream in 3+ years.

This tutorial would help you understand Deep learning frameworks, such as convolutional neural networks (CNNs), which have almost completely replaced other machine learning techniques for specific tasks such as image recognition using large training datasets. In this webinar, we will go over how CNNs, their training methods, and hardware evolved since LeNet first appeared in the late 1990’s. We will examine the challenges that came along, and some key innovations that helped overcome these challenges. We will also look at a guide on how to get started with CNNs, some common pitfalls, and tips and tricks in training CNNs. Advanced Technology Group (ATG) of the CTOOffice at NetApp. The ATG group is responsible for investigations, through early product prototypes, and leveraging technologies expected to become mainstream in 3+ years.

This Edureka "What isDeep Learning" video (Blog: https://goo.gl/4zxMfU) will help you to understand about the relationship between Deep Learning, Machine Learn...

This Edureka "What isDeep Learning" video (Blog: https://goo.gl/4zxMfU) will help you to understand about the relationship between Deep Learning, Machine Learning and Artificial Intelligence and how Deep Learning came into the picture. This tutorial will be discussing about Artificial Intelligence, Machine Learning and its limitations, how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning.
Below are the topics covered in this tutorial:
1. What Is Artificial Intelligence?
2. What Is Machine Learning?
3. Limitations Of Machine Learning
4. Deep Learning To The Rescue
5. What Is Deep Learning?
6. Deep Learning Applications
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

This Edureka "What isDeep Learning" video (Blog: https://goo.gl/4zxMfU) will help you to understand about the relationship between Deep Learning, Machine Learning and Artificial Intelligence and how Deep Learning came into the picture. This tutorial will be discussing about Artificial Intelligence, Machine Learning and its limitations, how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning.
Below are the topics covered in this tutorial:
1. What Is Artificial Intelligence?
2. What Is Machine Learning?
3. Limitations Of Machine Learning
4. Deep Learning To The Rescue
5. What Is Deep Learning?
6. Deep Learning Applications
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

2nd-order Optimization for Neural Network Training

Neural networks have become the main workhorse of supervised learning, and their efficient training is an important technical challenge which has received a lot...

Neural networks have become the main workhorse of supervised learning, and their efficient training is an important technical challenge which has received a lot of attention. While stochastic gradient descent (SGD) with momentum works well enough in many situations, its performance declines dramatically as networks become deeper and more complex. Given their success in other domains, 2nd-order optimization methods seem like a promising alternative. Unfortunately, the cost of inverting the curvature matrix (traditionally the Hessian) is prohibitive for neural networks, due to their high dimension. One common solution is to approximate the curvature matrix as diagonal or low-rank. Because such approximations are quite crude, most of the theoretical power of 2nd-order methods is lost, and experimental evidence suggests that they don't work much better than SGD. In this talk I will present two methods for achieving efficient and robust 2nd-order optimization for neural networks that do not rely on such approximations. The first, called Hessian-free Optimization (HF), is a truncated-Newton method which uses preconditioned conjugate gradient (CG) (in lieu of matrix inversion) to approximate the 2nd-order update, without making any approximations to the curvature matrix itself. Experiments show that HF works well, and in particular that it converges in orders of magnitude fewer iterations than SGD. While this makes a compelling case for the potential of 2nd-order methods, HF unfortunately suffers in practice due to the high cost of computing its updates (via multiple iterations of CG). The second method I will present, called Kronecker-Factored Approximate Curvature (K-FAC), gets around this issue by using a high quality approximation of the curvature matrix which is neither diagonal nor low-rank, but can nonetheless be inverted very efficiently. Experiments show that K-FAC significantly outperforms existing methods, and has the potential to be 20-100 times faster in a highly distributed setting.

Neural networks have become the main workhorse of supervised learning, and their efficient training is an important technical challenge which has received a lot of attention. While stochastic gradient descent (SGD) with momentum works well enough in many situations, its performance declines dramatically as networks become deeper and more complex. Given their success in other domains, 2nd-order optimization methods seem like a promising alternative. Unfortunately, the cost of inverting the curvature matrix (traditionally the Hessian) is prohibitive for neural networks, due to their high dimension. One common solution is to approximate the curvature matrix as diagonal or low-rank. Because such approximations are quite crude, most of the theoretical power of 2nd-order methods is lost, and experimental evidence suggests that they don't work much better than SGD. In this talk I will present two methods for achieving efficient and robust 2nd-order optimization for neural networks that do not rely on such approximations. The first, called Hessian-free Optimization (HF), is a truncated-Newton method which uses preconditioned conjugate gradient (CG) (in lieu of matrix inversion) to approximate the 2nd-order update, without making any approximations to the curvature matrix itself. Experiments show that HF works well, and in particular that it converges in orders of magnitude fewer iterations than SGD. While this makes a compelling case for the potential of 2nd-order methods, HF unfortunately suffers in practice due to the high cost of computing its updates (via multiple iterations of CG). The second method I will present, called Kronecker-Factored Approximate Curvature (K-FAC), gets around this issue by using a high quality approximation of the curvature matrix which is neither diagonal nor low-rank, but can nonetheless be inverted very efficiently. Experiments show that K-FAC significantly outperforms existing methods, and has the potential to be 20-100 times faster in a highly distributed setting.

ConvNetJS is a JavaScript library for training and running Convolutional Neural Networks in the browser. It can be used for common Machine Learning tasks, such as classification and regression and can get quite powerful using offline training. In this talk we will cover some basic theory, learn how and why to use CNNs in the browser and see some cool demos.
This great talk was held by ChristophKörner at the monthly ViennaJS meetup in Vienna: http://viennajs.org
The full slides from this talk can be found here: https://docs.google.com/presentation/d/1bLk23AF2lhH7t91vkW9HS-pYTYdNRriW-pjkC1GnK9E/

ConvNetJS is a JavaScript library for training and running Convolutional Neural Networks in the browser. It can be used for common Machine Learning tasks, such as classification and regression and can get quite powerful using offline training. In this talk we will cover some basic theory, learn how and why to use CNNs in the browser and see some cool demos.
This great talk was held by ChristophKörner at the monthly ViennaJS meetup in Vienna: http://viennajs.org
The full slides from this talk can be found here: https://docs.google.com/presentation/d/1bLk23AF2lhH7t91vkW9HS-pYTYdNRriW-pjkC1GnK9E/

published:04 Jun 2016

views:4004

back

TensorFlow and Deep Learning without a PhD, Part 1 (Google Cloud Next '17)

With TensorFlow, deep machine learning transitions from an area of research to mainstream software engineering. In this video, Martin Gorner demonstrates how to...

With TensorFlow, deep machine learning transitions from an area of research to mainstream software engineering. In this video, Martin Gorner demonstrates how to construct and train a neural network that recognises handwritten digits. Along the way, he'll describe some "tricks of the trade" used in neural network design, and finally, he'll bring the recognition accuracy of his model above 99%.
Content applies to software developers of all levels. Experienced machine learning enthusiasts, this video will introduce you to TensorFlow through well known models such as dense and convolutional networks. This is an intense technical video designed to help beginners in machine learning ramp up quickly.
Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h
Watch more talks about Big Data & Machine Learning here: https://goo.gl/OcqI9k

With TensorFlow, deep machine learning transitions from an area of research to mainstream software engineering. In this video, Martin Gorner demonstrates how to construct and train a neural network that recognises handwritten digits. Along the way, he'll describe some "tricks of the trade" used in neural network design, and finally, he'll bring the recognition accuracy of his model above 99%.
Content applies to software developers of all levels. Experienced machine learning enthusiasts, this video will introduce you to TensorFlow through well known models such as dense and convolutional networks. This is an intense technical video designed to help beginners in machine learning ramp up quickly.
Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h
Watch more talks about Big Data & Machine Learning here: https://goo.gl/OcqI9k

This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. MotivationBehind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learni...Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

13:01

Neural network tutorial: The back-propagation algorithm (Part 1)

In this video we will derive the back-propagation algorithm as is used for neural networks...

Neural network tutorial: The back-propagation algorithm (Part 1)

In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but the derivation is the same, and easily extensible.
Helpful diagram: https://www.dropbox.com/s/vj0qg9jlmy3mwof/Explanation_1.pdf?dl=0
This particular video goes from the derivative of the sigmoid itself to the delta for the output layer
The presentation can be found here: https://www.dropbox.com/s/z5bz0cw0boxxon1/BackPropagation.pdf?dl=0

4:45

Introduction to Deep Learning: What Are Convolutional Neural Networks?

Introduction to Deep Learning: What Are Convolutional Neural Networks?

Get free deep learning resources: https://goo.gl/Z6vLDU
Explore the basics behind convolutional neural networks (CNNs) in this MATLAB® Tech Talk. Broadly, CNNs are a common deep learning architecture – but what exactly is a CNN? This video breaks down this sometimes complicated concept into easy-to-understand parts. You’ll learn about 3 concepts: local receptive fields, shared weights and biases, and activation and pooling.
The video pulls together these three concepts and shows you how to configure the layers in a CNN.
You’ll also learn about the 3 ways to train CNNs for image analysis. These include: 1.) Training the model from scratch; 2.) Using transfer learning (based on the idea that you can use knowledge of one type of problem to solve a similar problem); 3.) Using a pretrained CNN to extract features for training a machine learning model.

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

5:39

How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)

A good model follows the “Goldilocks” principle in terms of data fitting. Models that unde...

How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is female, it is technically referred to as a lioness. Since this model does not take all necessary features into account while performing classification, it is said to underfit the data. The problem of underfitting is solved by adding more detail to the model to ensure that it properly captures the differences between classes.
On the other hand, a model may also consider every possible detail and develop very specific, complex rules for classification. For example, if one data point represents a lion that is 3.5 feet tall, weighs 305 pounds, and has 2.9-inch claws, the model may develop a rule that classifies every 3.5 foot tall, 305 pound cat with 2.9-inch claws as a lion. Such rules will accurately classify the training data, but will poorly generalize to new data samples. A model that develops these kinds of rules is said to overfit the data. In other words, the model has failed to identify the true patterns that differentiate the classes. As a separate example, if the data only contained tigers that grew up in a zoo, the model may have difficulty classifying tigers that grew up in the wild. So while improving the process of data collection is helpful to prevent this problem, the model must be designed to identify the most important patterns that identify a class, so that new samples can be properly classified.
With regards to neural networks, overfitting typically stems from too many input features, or the use of an overly-complicated network configuration. If the input count is too large, the training process may start to assign weights to features that either aren't needed or add unnecessary complexity to the model. An overly-complicated configuration may lead to the development of specific rules that improperly relate many different features, resulting in poor generalization.
Overfitting is a common problem in data science. One popular method to reduce overfitting is the use of a cross-validation data set along with parameter averaging. For neural networks, a common method is regularization. There are different types such as L1 and L2, but each of these follows the same principle – penalize the model for letting weights and biases become too large. Another method is MaxNorm constraints, which directly adds a size limit to the weights and biases. A different approach is dropout, which randomly switches off certain neurons in the network, preventing the model from becoming too dependent on a set of neurons and the associated weights and biases. While these methods are broadly applied across the model rather than used for systematically searching for problem patterns, they have been proven to reduce and sometimes prevent the problem of overfitting.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

3:26

Neural Network Written Rap Song

Excluding the beat; this song was written 100% by a deep neural network (if you aren't fam...

Neural Network Written Rap Song

Excluding the beat; this song was written 100% by a deep neural network (if you aren't familiar with neural networks, just think artificial intelligence).
Source Code: https://github.com/robbiebarrat/rapping-neural-network
Lyrics: I wrote a neural network, which wrote this after being introduced to ~6,000 lines of Kanye West.
Beat: https://youtu.be/Vt8OM41TytU (modified - I took out some of the quiet parts so it was a more constant beat)
This video is a recording of "SpeechTest.py"
By Robbie Barrat, 2017: RobbieBarrat@gmail.com
The following is part of the output of "DeepRap.py"
----------------------------------------------------
To all my guys
You messin up my eyes
Fuck up your sleeves
And fire up the skies
...
They steal your whole sound, that's a Real WorldChallenge
You an addict of cheating but I took the charge
Stop running up my money tall, height advantage
Ohhhhh, ohhhh; ohhh you too much to handle in one package
Not now but right now, you know... I had to go downtown
Shorty come and fix you up like bobby brown
Then he help me get my back up in this part of town
I can make you come and fix you up like bobby brown
Driving in the sky, you got a new crib to hold down
Shit's about to go and put my mack berry down
...
So dangerous, but it's all terrific
I done wore designers I won't get specific
It seem like fine hoes is hard to get some traffic
He claim her heart colder than the music
Man I wish I had to play in traffic
You know when I heard it's the way we was magic
When see me with a spit like a paraplegic
Wonder why it so hard not to play in traffic
Really Doe got a hell of a romantic
Lifestyles of the egotistic
...
The Delorean parked in front of that wack shit
The doctor don't wanna hear that official shit
I'm in pajamas but I just want my credit
If this party ain't got it I'm killing this shit
There's only one girl on my Brad Pitt
Keep it up some god damn it I'm killing this shit
And I'm feeling smooth as the chorus hit
My homie called me like I owe her shit
But I need a girl who barely bra fit
Me and Common is back on my Brad Pitt
Man, you don't get caught up with stupid shit
Then she cut from a girl who barely bra fit
How it feel to wake up with on some silly shit
I wish you could get is a whole city lit
That's where I see they got it I'm killing this shit
Yeah you got a Benz and they ain't done shit
Reality is catching up with stupid shit
Maybe that'll work when you check, they ain't done shit
...
I use it as always nigga, as always
Made her sit in that motherfucker sideways
Oh girl your silhouette make me pull the toys
I wonder where they want her to Ruby TuesdaysTell me what we at a ring from Tiffany's
Yeah, tonight I see you in my home gym
When we die, the money should've changed him
I'm on TV talking like it's all in my home gym
And see Penthouse 3 where a nigga from my team
He nicknamed me K-Rock so they abbreviate him
I don't even want to talk some sense in him
Welcome to the most and they ain't expect from him
The next generation will be assaulting him
...

0:38

Neural network Meaning

Video shows what neural network means. A real or virtual computer system designed to emula...

Neural network Meaning

Video shows what neural network means. A real or virtual computer system designed to emulate the brain in its ability to "learn" to assess imprecise data.. Any network of neurons etc that function together to achieve a common purpose.. neural network synonyms: neural net, artificial neural network, simulated neural network. Neural network Meaning. How to pronounce, definition audio dictionary. How to say neural network. Powered by MaryTTS, Wiktionary

Cadence Demonstration of a Neural Network on the Vision P6 DSP

For more information about embedded vision, including hundreds of additional videos, please visit http://www.embedded-vision.com.
MeghaDagha , Senior Technical Marketing Manager at Cadence, demonstrates the company's latest embedded vision technologies and products at the May 2017 EmbeddedVisionSummit. Specifically, Daga demonstrates Cadence’s highly programmable, efficient Tensilica® Vision P6 DSP by showing an implementation of one of the most common neural networks, AlexNet. The demo uses a trained floating-point neural network from Caffe. Tensilica provided the most optimal solution by using their custom quantization to 8 bits with less than 1% accuracy drop. By using quantized coefficients, the Vision P6 DSP was able to achieve low bandwidth, low power, and high performance at minimal degradation.

6:45

Topic 23 - Activating a relaxed state of mind

The mind and body share one common neural network. Irritation within your mind flows direc...

Topic 23 - Activating a relaxed state of mind

The mind and body share one common neural network. Irritation within your mind flows directly into your body and increases your sensitivity to pain and the tendency for muscle spasm. Dr Anthony A Blisko describes how to quiet your mind in order to experience a healthier state of ease within both your mind and body.

This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. MotivationBehind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learni...Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

13:01

Neural network tutorial: The back-propagation algorithm (Part 1)

In this video we will derive the back-propagation algorithm as is used for neural networks...

Neural network tutorial: The back-propagation algorithm (Part 1)

In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but the derivation is the same, and easily extensible.
Helpful diagram: https://www.dropbox.com/s/vj0qg9jlmy3mwof/Explanation_1.pdf?dl=0
This particular video goes from the derivative of the sigmoid itself to the delta for the output layer
The presentation can be found here: https://www.dropbox.com/s/z5bz0cw0boxxon1/BackPropagation.pdf?dl=0

4:45

Introduction to Deep Learning: What Are Convolutional Neural Networks?

Introduction to Deep Learning: What Are Convolutional Neural Networks?

Get free deep learning resources: https://goo.gl/Z6vLDU
Explore the basics behind convolutional neural networks (CNNs) in this MATLAB® Tech Talk. Broadly, CNNs are a common deep learning architecture – but what exactly is a CNN? This video breaks down this sometimes complicated concept into easy-to-understand parts. You’ll learn about 3 concepts: local receptive fields, shared weights and biases, and activation and pooling.
The video pulls together these three concepts and shows you how to configure the layers in a CNN.
You’ll also learn about the 3 ways to train CNNs for image analysis. These include: 1.) Training the model from scratch; 2.) Using transfer learning (based on the idea that you can use knowledge of one type of problem to solve a similar problem); 3.) Using a pretrained CNN to extract features for training a machine learning model.

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

5:39

How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)

A good model follows the “Goldilocks” principle in terms of data fitting. Models that unde...

How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is female, it is technically referred to as a lioness. Since this model does not take all necessary features into account while performing classification, it is said to underfit the data. The problem of underfitting is solved by adding more detail to the model to ensure that it properly captures the differences between classes.
On the other hand, a model may also consider every possible detail and develop very specific, complex rules for classification. For example, if one data point represents a lion that is 3.5 feet tall, weighs 305 pounds, and has 2.9-inch claws, the model may develop a rule that classifies every 3.5 foot tall, 305 pound cat with 2.9-inch claws as a lion. Such rules will accurately classify the training data, but will poorly generalize to new data samples. A model that develops these kinds of rules is said to overfit the data. In other words, the model has failed to identify the true patterns that differentiate the classes. As a separate example, if the data only contained tigers that grew up in a zoo, the model may have difficulty classifying tigers that grew up in the wild. So while improving the process of data collection is helpful to prevent this problem, the model must be designed to identify the most important patterns that identify a class, so that new samples can be properly classified.
With regards to neural networks, overfitting typically stems from too many input features, or the use of an overly-complicated network configuration. If the input count is too large, the training process may start to assign weights to features that either aren't needed or add unnecessary complexity to the model. An overly-complicated configuration may lead to the development of specific rules that improperly relate many different features, resulting in poor generalization.
Overfitting is a common problem in data science. One popular method to reduce overfitting is the use of a cross-validation data set along with parameter averaging. For neural networks, a common method is regularization. There are different types such as L1 and L2, but each of these follows the same principle – penalize the model for letting weights and biases become too large. Another method is MaxNorm constraints, which directly adds a size limit to the weights and biases. A different approach is dropout, which randomly switches off certain neurons in the network, preventing the model from becoming too dependent on a set of neurons and the associated weights and biases. While these methods are broadly applied across the model rather than used for systematically searching for problem patterns, they have been proven to reduce and sometimes prevent the problem of overfitting.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

3:26

Neural Network Written Rap Song

Excluding the beat; this song was written 100% by a deep neural network (if you aren't fam...

Neural Network Written Rap Song

Excluding the beat; this song was written 100% by a deep neural network (if you aren't familiar with neural networks, just think artificial intelligence).
Source Code: https://github.com/robbiebarrat/rapping-neural-network
Lyrics: I wrote a neural network, which wrote this after being introduced to ~6,000 lines of Kanye West.
Beat: https://youtu.be/Vt8OM41TytU (modified - I took out some of the quiet parts so it was a more constant beat)
This video is a recording of "SpeechTest.py"
By Robbie Barrat, 2017: RobbieBarrat@gmail.com
The following is part of the output of "DeepRap.py"
----------------------------------------------------
To all my guys
You messin up my eyes
Fuck up your sleeves
And fire up the skies
...
They steal your whole sound, that's a Real WorldChallenge
You an addict of cheating but I took the charge
Stop running up my money tall, height advantage
Ohhhhh, ohhhh; ohhh you too much to handle in one package
Not now but right now, you know... I had to go downtown
Shorty come and fix you up like bobby brown
Then he help me get my back up in this part of town
I can make you come and fix you up like bobby brown
Driving in the sky, you got a new crib to hold down
Shit's about to go and put my mack berry down
...
So dangerous, but it's all terrific
I done wore designers I won't get specific
It seem like fine hoes is hard to get some traffic
He claim her heart colder than the music
Man I wish I had to play in traffic
You know when I heard it's the way we was magic
When see me with a spit like a paraplegic
Wonder why it so hard not to play in traffic
Really Doe got a hell of a romantic
Lifestyles of the egotistic
...
The Delorean parked in front of that wack shit
The doctor don't wanna hear that official shit
I'm in pajamas but I just want my credit
If this party ain't got it I'm killing this shit
There's only one girl on my Brad Pitt
Keep it up some god damn it I'm killing this shit
And I'm feeling smooth as the chorus hit
My homie called me like I owe her shit
But I need a girl who barely bra fit
Me and Common is back on my Brad Pitt
Man, you don't get caught up with stupid shit
Then she cut from a girl who barely bra fit
How it feel to wake up with on some silly shit
I wish you could get is a whole city lit
That's where I see they got it I'm killing this shit
Yeah you got a Benz and they ain't done shit
Reality is catching up with stupid shit
Maybe that'll work when you check, they ain't done shit
...
I use it as always nigga, as always
Made her sit in that motherfucker sideways
Oh girl your silhouette make me pull the toys
I wonder where they want her to Ruby TuesdaysTell me what we at a ring from Tiffany's
Yeah, tonight I see you in my home gym
When we die, the money should've changed him
I'm on TV talking like it's all in my home gym
And see Penthouse 3 where a nigga from my team
He nicknamed me K-Rock so they abbreviate him
I don't even want to talk some sense in him
Welcome to the most and they ain't expect from him
The next generation will be assaulting him
...

0:38

Neural network Meaning

Video shows what neural network means. A real or virtual computer system designed to emula...

Neural network Meaning

Video shows what neural network means. A real or virtual computer system designed to emulate the brain in its ability to "learn" to assess imprecise data.. Any network of neurons etc that function together to achieve a common purpose.. neural network synonyms: neural net, artificial neural network, simulated neural network. Neural network Meaning. How to pronounce, definition audio dictionary. How to say neural network. Powered by MaryTTS, Wiktionary

This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. MotivationBehind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learni...Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

58:12

Neural Networks Explained in Plain English w/Ron Leplae

Visit https://futures.io for more futures trading webinars. This webinar was originally u...

This Edureka "Deep Learning Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Do watch this tutorial till the end to see all the practical demonstration.
Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. SingleLayer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Tutorial on Convolutional Neural Networks(CNNs) for image recognition

This tutorial would help you understand Deep learning frameworks, such as convolutional neural networks (CNNs), which have almost completely replaced other machine learning techniques for specific tasks such as image recognition using large training datasets. In this webinar, we will go over how CNNs, their training methods, and hardware evolved since LeNet first appeared in the late 1990’s. We will examine the challenges that came along, and some key innovations that helped overcome these challenges. We will also look at a guide on how to get started with CNNs, some common pitfalls, and tips and tricks in training CNNs. Advanced Technology Group (ATG) of the CTOOffice at NetApp. The ATG group is responsible for investigations, through early product prototypes, and leveraging technologies expected to become mainstream in 3+ years.

This Edureka "What isDeep Learning" video (Blog: https://goo.gl/4zxMfU) will help you to understand about the relationship between Deep Learning, Machine Learning and Artificial Intelligence and how Deep Learning came into the picture. This tutorial will be discussing about Artificial Intelligence, Machine Learning and its limitations, how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning.
Below are the topics covered in this tutorial:
1. What Is Artificial Intelligence?
2. What Is Machine Learning?
3. Limitations Of Machine Learning
4. Deep Learning To The Rescue
5. What Is Deep Learning?
6. Deep Learning Applications
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of OnlineLive Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVETechnical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a VerifiableCertificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “HelloWord” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'DataScientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. InformationArchitects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information.
Website: https://www.edureka.co/ai-deep-learning-with-tensorflow
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

1:27:22

2nd-order Optimization for Neural Network Training

Neural networks have become the main workhorse of supervised learning, and their efficient...

2nd-order Optimization for Neural Network Training

Neural networks have become the main workhorse of supervised learning, and their efficient training is an important technical challenge which has received a lot of attention. While stochastic gradient descent (SGD) with momentum works well enough in many situations, its performance declines dramatically as networks become deeper and more complex. Given their success in other domains, 2nd-order optimization methods seem like a promising alternative. Unfortunately, the cost of inverting the curvature matrix (traditionally the Hessian) is prohibitive for neural networks, due to their high dimension. One common solution is to approximate the curvature matrix as diagonal or low-rank. Because such approximations are quite crude, most of the theoretical power of 2nd-order methods is lost, and experimental evidence suggests that they don't work much better than SGD. In this talk I will present two methods for achieving efficient and robust 2nd-order optimization for neural networks that do not rely on such approximations. The first, called Hessian-free Optimization (HF), is a truncated-Newton method which uses preconditioned conjugate gradient (CG) (in lieu of matrix inversion) to approximate the 2nd-order update, without making any approximations to the curvature matrix itself. Experiments show that HF works well, and in particular that it converges in orders of magnitude fewer iterations than SGD. While this makes a compelling case for the potential of 2nd-order methods, HF unfortunately suffers in practice due to the high cost of computing its updates (via multiple iterations of CG). The second method I will present, called Kronecker-Factored Approximate Curvature (K-FAC), gets around this issue by using a high quality approximation of the curvature matrix which is neither diagonal nor low-rank, but can nonetheless be inverted very efficiently. Experiments show that K-FAC significantly outperforms existing methods, and has the potential to be 20-100 times faster in a highly distributed setting.

34:42

ConvNetJS – Deep Learning in your browser

ConvNetJS is a JavaScript library for training and running Convolutional Neural Networks i...

ConvNetJS – Deep Learning in your browser

ConvNetJS is a JavaScript library for training and running Convolutional Neural Networks in the browser. It can be used for common Machine Learning tasks, such as classification and regression and can get quite powerful using offline training. In this talk we will cover some basic theory, learn how and why to use CNNs in the browser and see some cool demos.
This great talk was held by ChristophKörner at the monthly ViennaJS meetup in Vienna: http://viennajs.org
The full slides from this talk can be found here: https://docs.google.com/presentation/d/1bLk23AF2lhH7t91vkW9HS-pYTYdNRriW-pjkC1GnK9E/

55:52

TensorFlow and Deep Learning without a PhD, Part 1 (Google Cloud Next '17)

With TensorFlow, deep machine learning transitions from an area of research to mainstream ...

TensorFlow and Deep Learning without a PhD, Part 1 (Google Cloud Next '17)

With TensorFlow, deep machine learning transitions from an area of research to mainstream software engineering. In this video, Martin Gorner demonstrates how to construct and train a neural network that recognises handwritten digits. Along the way, he'll describe some "tricks of the trade" used in neural network design, and finally, he'll bring the recognition accuracy of his model above 99%.
Content applies to software developers of all levels. Experienced machine learning enthusiasts, this video will introduce you to TensorFlow through well known models such as dense and convolutional networks. This is an intense technical video designed to help beginners in machine learning ramp up quickly.
Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h
Watch more talks about Big Data & Machine Learning here: https://goo.gl/OcqI9k

Artificial Neural Network Tutorial | Deep Learning...

Neural Networks Explained in Plain English w/Ron L...

Deep Learning Tutorial | Deep Learning Tutorial fo...

Neural Networks and Machine Learning Building Int...

Neural Net in C++ Tutorial on Vimeo...

Tutorial on Convolutional Neural Networks(CNNs) fo...

What is Deep Learning | Deep Learning Simplified |...

2nd-order Optimization for Neural Network Training...

ConvNetJS – Deep Learning in your browser...

TensorFlow and Deep Learning without a PhD, Part 1...

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